Synopsis

This book, "Technical Design for an Automated Spoken English Assessment System" by Vikram Singh Sankhala, presents a comprehensive technical blueprint for building a CEFR-aligned spoken English assessment system. It details a multi-modal, multi-task transformer architecture integrating cutting-edge AI models like data2vec2 for speech encoding, an M-adapter for modality fusion, and a large language model (LLM) for scoring. The design outlines the core components including a specialized Accent Classifier (MPSA-DenseNet) and a Phoneme-level Scorer, detailing the data flow pipeline from raw audio input through feature extraction, multi-modal fusion, and independent scoring modules, culminating in aggregated results and explainable reports for candidates and administrators. The book elaborates on the proposed multi-stage model training strategy, covering pre-training for robust feature extraction and fine-tuning on labeled assessment data for accurate multi-task prediction (CEFR, sub-skills). It also addresses practical deployment considerations, suggesting a microservices architecture on cloud platforms like Google Cloud Vertex AI for scalability and real-time inference. By presenting this detailed technical architecture and implementation strategy, the book provides a clear roadmap for developers and researchers aiming to build a robust, deployable automated spoken English assessment application capable of delivering granular, multi-faceted feedback.

Technical Design for an Automated Spoken English Assessment System

Vikram Singh Sankhala

English

Technical Design for an Automated Spoken English Assessment System

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